Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-05-15"
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 4063 2020-05-15 New York 36 350951 27755
## 4061 2020-05-15 New Jersey 34 143905 10138
## 4052 2020-05-15 Massachusetts 25 83421 5592
## 4053 2020-05-15 Michigan 26 49982 4825
## 4070 2020-05-15 Pennsylvania 42 64178 4432
## 4044 2020-05-15 Illinois 17 90529 4075
## 4036 2020-05-15 Connecticut 9 36085 3285
## 4034 2020-05-15 California 6 77015 3192
## 4049 2020-05-15 Louisiana 22 33837 2382
## 4039 2020-05-15 Florida 12 44130 1916
## 4051 2020-05-15 Maryland 24 37105 1911
## 4045 2020-05-15 Indiana 18 27281 1691
## 4067 2020-05-15 Ohio 39 26956 1581
## 4040 2020-05-15 Georgia 13 35242 1563
## 4076 2020-05-15 Texas 48 46987 1300
## 4035 2020-05-15 Colorado 8 21207 1150
## 4081 2020-05-15 Washington 53 19230 1008
## 4080 2020-05-15 Virginia 51 28672 977
## 4054 2020-05-15 Minnesota 27 14249 692
## 4064 2020-05-15 North Carolina 37 17190 660
## 4032 2020-05-15 Arizona 4 13169 651
## 4056 2020-05-15 Missouri 29 10567 581
## 4055 2020-05-15 Mississippi 28 10801 493
## 4030 2020-05-15 Alabama 1 11373 483
## 4072 2020-05-15 Rhode Island 44 12219 479
## 4083 2020-05-15 Wisconsin 55 11854 445
## 4073 2020-05-15 South Carolina 45 8407 380
## 4038 2020-05-15 District of Columbia 11 6871 368
## 4059 2020-05-15 Nevada 32 6744 345
## 4048 2020-05-15 Kentucky 21 7578 343
For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
First check the 30 counties with the largest number of deaths.
## date county state fips cases deaths
## 146045 2020-05-15 New York City New York NA 195472 19972
## 144901 2020-05-15 Cook Illinois 17031 59905 2762
## 146044 2020-05-15 Nassau New York 36059 38864 2499
## 145571 2020-05-15 Wayne Michigan 26163 18882 2192
## 146064 2020-05-15 Suffolk New York 36103 37719 1757
## 144507 2020-05-15 Los Angeles California 6037 36259 1755
## 145971 2020-05-15 Essex New Jersey 34013 15953 1510
## 145966 2020-05-15 Bergen New Jersey 34003 17195 1443
## 146072 2020-05-15 Westchester New York 36119 31942 1392
## 145486 2020-05-15 Middlesex Massachusetts 25017 18683 1347
## 144606 2020-05-15 Fairfield Connecticut 9001 14009 1109
## 145973 2020-05-15 Hudson New Jersey 34017 17237 1042
## 144607 2020-05-15 Hartford Connecticut 9003 8126 1025
## 146457 2020-05-15 Philadelphia Pennsylvania 42101 19349 1021
## 145984 2020-05-15 Union New Jersey 34039 14492 939
## 145552 2020-05-15 Oakland Michigan 26125 7994 896
## 145976 2020-05-15 Middlesex New Jersey 34023 14429 865
## 145980 2020-05-15 Passaic New Jersey 34031 14930 816
## 144610 2020-05-15 New Haven Connecticut 9009 9881 783
## 145490 2020-05-15 Suffolk Massachusetts 25025 15996 768
## 145482 2020-05-15 Essex Massachusetts 25009 12131 751
## 145539 2020-05-15 Macomb Michigan 26099 6274 729
## 145488 2020-05-15 Norfolk Massachusetts 25021 7331 710
## 145979 2020-05-15 Ocean New Jersey 34029 7829 610
## 146452 2020-05-15 Montgomery Pennsylvania 42091 5697 608
## 145978 2020-05-15 Morris New Jersey 34027 5990 550
## 144662 2020-05-15 Miami-Dade Florida 12086 15010 548
## 145492 2020-05-15 Worcester Massachusetts 25027 8786 538
## 147079 2020-05-15 King Washington 53033 7679 523
## 145034 2020-05-15 Marion Indiana 18097 8082 500
For these 30 counties, I check the number of new cases and the number of new deaths.
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
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## locale:
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
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## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4